DETAILED ACTION
This action is in reply to the Amendments filed on 08/03/2026.
Claims 1-20 are rejected.
Claims 1-20 are currently pending and have been examined.
Response to Amendment
Applicant’s amendment, filed 08/03/2026, has been entered. Claims 1, 12, and 20 have been amended.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Under Step 1 of the Subject Matter Eligibility Test for Products and Processes, the claims must be directed to one of the four statutory categories (see MPEP 2106.03). All the claims are directed to one of the four statutory categories (YES).
Under Step 2A of the Subject Matter Eligibility Test, it is determined whether the claims are directed to a judicially recognized exception (see MPEP 2106.04). Step 2A is a two-prong inquiry.
Under Prong 1, it is determined whether the claim recites a judicial exception (YES). Taking Claim 1 as representative, the claim recites limitations that fall within the certain methods of organizing human activity groupings of abstract ideas, including:
-applying an order classification model to characteristics of a customer and attributes of one or more orders received from the customer to select an order, wherein one or more other orders are eliminated from a set of orders used to identify churn of the customer responsive to being classified as sporadic using the order classification model, the order classification model trained to classify[ing] orders placed in connection with an atypical condition of the customer as sporadic;
-identifying churn of a customer based on the order selected by applying the order classification model, wherein churn of the customer indicates a decrease in interaction by the customer with the computer system;
-applying a churn classification model to identify an event to which the churn of the customer is attributed, wherein the churn classification model is trained to determine[s] an event to which churn of the customer is attributed based on characteristics of the customer and attributes describing fulfillment of one or more orders received from the customer;
-generating a picker score for each of a set of pickers, the picker score for a picker comprising a probability of the customer performing a specific action within a threshold time period of the picker fulfilling a subsequent order from the customer, the picker score generated by applying a picker scoring model to the characteristics of the customer, characteristics of the picker, the identified event determined by applying the churn classification model, and one or more prior orders from the customer,
-the picker scoring model trained and retrained by:
-obtaining a score training dataset including a score training example, the score training dataset excluding orders classified as sporadic by the order classification model, wherein the score training dataset includes characteristics of a customer included in the score training example, characteristics of a picker included in the score training example, an event to which churn of the customer included in the score training example was attributed, and one or more prior orders from the customer included in the score training example, each score training example having a label indicating whether the customer in the score training example performed the specific action within the threshold time period after fulfillment of an order by the picker in the score training example;
-applying the picker scoring model to each training example of the score training dataset to generate a predicted probability of the customer in the score training example performing the specific action within the threshold time period after fulfillment of an order by the picker in the score training example;
-scoring output of the picker scoring model using a loss function and the label of the score training example; and
-updating one or more parameters of the picker scoring model by backpropagation based on the scoring until one or more criteria are satisfied;
-selecting, based on the picker scores, a picker of the set of pickers to fulfill the subsequent order from the customer;
-in response to receiving the subsequent order from the customer, transmitting the subsequent order to a client device associated with the selected picker for display;
-collecting data describing whether the customer performs the specific action within the threshold time period after fulfillment of the subsequent order by the selected picker; and
-generating a new training example based on the collected data for [updating] training or retraining one or more of the picker scoring model, the order classification model, or the churn classification model
The above limitations recite the concept of identifying churn of a customer by classifying the customer, identifying an event attributed to the churn, determining a picker to fulfill the subsequent order from the customer. The above limitations fall within the “Certain Methods of Organizing Human Activity” groupings of abstract ideas, enumerated in MPEP 2106.04(a).
Certain methods of organizing human activity include:
fundamental economic principles or practices (including hedging, insurance, and mitigating risk)
commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; and business relations)
managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)
The limitations of generating a picker score for each of a set of pickers, the picker score for a picker comprising a probability of the customer performing a specific action within a threshold time period of the picker fulfilling a subsequent order from the customer, the picker score generated by applying a picker scoring model to the characteristics of the customer, characteristics of the picker, the identified event determined by applying the churn classification model, and one or more prior orders from the customer; selecting, based on the picker scores, a picker of the set of pickers to fulfill the subsequent order from the customer; and collecting data describing whether the customer performs the specific action within the threshold time period after fulfillment of the subsequent order by the selected picker are processes that, under their broadest reasonable interpretation, cover a commercial interaction. For example, “generating,” “selecting,” and “collecting” in the context of this claim encompass advertising, and marketing or sales activities.
Similarly, the limitations of applying an order classification model to characteristics of a customer and attributes of one or more orders received from the customer to select an order, wherein one or more other orders are eliminated from a set of orders used to identify churn of the customer responsive to being classified as sporadic using the order classification model, the order classification model trained to classify[ing] orders placed in connection with an atypical condition of the customer as sporadic; identifying churn of a customer based on the order selected by applying the order classification model, wherein churn of the customer indicates a decrease in interaction by the customer with the computer system; applying a churn classification model to identify an event to which the churn of the customer is attributed, wherein the churn classification model is trained to determine[s] an event to which churn of the customer is attributed based on characteristics of the customer and attributes describing fulfillment of one or more orders received from the customer; the picker scoring model trained and retrained by: obtaining a score training dataset including a score training example, the score training dataset excluding orders classified as sporadic by the order classification model, wherein the score training dataset includes characteristics of a customer included in the score training example, characteristics of a picker included in the score training example, an event to which churn of the customer included in the score training example was attributed, and one or more prior orders from the customer included in the score training example, each score training example having a label indicating whether the customer in the score training example performed the specific action within the threshold time period after fulfillment of an order by the picker in the score training example; applying the picker scoring model to each training example of the score training dataset to generate a predicted probability of the customer in the score training example performing the specific action within the threshold time period after fulfillment of an order by the picker in the score training example; scoring output of the picker scoring model using a loss function and the label of the score training example; and updating one or more parameters of the picker scoring model by backpropagation based on the scoring until one or more criteria are satisfied; in response to receiving the subsequent order from the customer, transmitting the subsequent order to a client device associated with the selected picker for display; and generating a new training example based on the collected data for [updating] training or retraining one or more of the picker scoring model, the order classification model, or the churn classification model are processes that, under their broadest reasonable interpretation, cover a commercial interaction. That is, other than reciting that the order classification model is trained, that the system is a computer system, that the churn classification model is trained, that the picker scoring model is trained and retrained, that the score dataset is a score training dataset, that the score examples are score training examples, that the picker scoring model uses a loss function, that the one or more parameters of the picker scoring model are updated by backpropagation, that the subsequent order is transmitted to a client device associated with the selected picker, that the new example is a new training example, and that the collected data is for training or retraining, nothing in the claim element precludes the step from practically being performed by people. For example, but for the “computer system,” “trained,” “trained and retrained by,” “training dataset,” “score training examples,” “each score training example,” “score training example,” “loss function,” “backpropagation,” “a client device associated with the selected picker,” “a new training example,” and “training or retraining” language, “applying,” “identifying,” “obtaining,” “applying,” “scoring,” “updating,” and “transmitting,” and “generating” in the context of this claim encompasses advertising, and marketing or sales activities.
Under Prong 2, it is determined whether the claim recites additional elements that integrate the exception into a practical application of the exception. This judicial exception is not integrated into a practical application (NO).
-applying an order classification model to characteristics of a customer and attributes of one or more orders received from the customer to select an order, wherein one or more other orders are eliminated from a set of orders used to identify churn of the customer responsive to being classified as sporadic using the order classification model, the order classification model trained to classify orders placed in connection with an atypical condition of the customer as sporadic;
-identifying churn of a customer based on the order selected by applying the order classification model, wherein churn of the customer indicates a decrease in interaction by the customer with the computer system;
-applying a churn classification model to identify an event to which the churn of the customer is attributed, wherein the churn classification model is trained to determine an event to which churn of the customer is attributed based on characteristics of the customer and attributes describing fulfillment of one or more orders received from the customer;
-generating a picker score for each of a set of pickers, the picker score for a picker comprising a probability of the customer performing a specific action within a threshold time period of the picker fulfilling a subsequent order from the customer, the picker score generated by applying a picker scoring model to the characteristics of the customer, characteristics of the picker, the identified event determined by applying the churn classification model, and one or more prior orders from the customer,
-the picker scoring model trained and retrained by:
-obtaining a score training dataset including a score training example, the score training dataset excluding orders classified as sporadic by the order classification model, wherein the score training dataset includes characteristics of a customer included in the score training example, characteristics of a picker included in the score training example, an event to which churn of the customer included in the score training example was attributed, and one or more prior orders from the customer included in the score training example, each score training example having a label indicating whether the customer in the score training example performed the specific action within the threshold time period after fulfillment of an order by the picker in the score training example;
-applying the picker scoring model to each training example of the score training dataset to generate a predicted probability of the customer in the score training example performing the specific action within the threshold time period after fulfillment of an order by the picker in the score training example;
-scoring output of the picker scoring model using a loss function and the label of the score training example; and
-updating one or more parameters of the picker scoring model by backpropagation based on the scoring until one or more criteria are satisfied;
-selecting, based on the picker scores, a picker of the set of pickers to fulfill the subsequent order from the customer;
-in response to receiving the subsequent order from the customer, transmitting the subsequent order to a client device associated with the selected picker for display;
-collecting data describing whether the customer performs the specific action within the threshold time period after fulfillment of the subsequent order by the selected picker; and
-generating a new training example based on the collected data for training or retraining one or more of the picker scoring model, the order classification model, or the churn classification model
These limitations are not indicative of integration into a practical application because:
The additional elements of claim 1 are recited at a high level of generality (i.e. as generic computing hardware) such that they amount to nothing more than mere instructions to implement or apply the abstract idea on a generic computing hardware (or, merely use a computer as a tool to perform an abstract idea) as supported by paragraph [0115] of Applicant’s specification – “Any of the steps, operations, or processes described herein may be performed or implemented with one or more hardware or software modules, alone or in combination with other devices. In some embodiments, a software module is implemented with a computer program product comprising one or more computer-readable media storing computer program code or instructions, which can be executed by a computer processor for performing any or all of the steps, operations, or processes described.” Specifically, the additional elements of a computer system, a processor, a computer-readable medium, the order classification model being trained, a computer system, the churn classification model being trained, trained and retrained by, a training dataset, score training examples, each score training example, a score training example, a loss function, backpropagation, a client device associated with the selected picker, a new training example, and training or retraining are recited at a high-level of generality (i.e. as a generic processor performing the generic computer functions of applying data, identifying data, generating data, obtaining data, scoring data, updating data, selecting data, transmitting data, collecting data, and generating data) such that they amount do no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. Further, the additional elements do no more than generally link the use of the judicial exception to a particular technological environment or field of use (such as computers or computing networks). For example, stating that the system is computer system only generally links the commercial interactions to a computer environment. Employing well-known computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment, does not integrate the exception into a practical application.
Additionally, the additional elements are insufficient to integrate the abstract idea into a practical application because the claim fails to i) reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, ii) apply the judicial exception with, or use the judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, iii) effect a transformation or reduction of a particular article to a different state or thing, or iv) apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, the judicial exception is not integrated into a practical application.
Under Step 2B, it is determined whether the claims recite additional elements that amount to significantly more than the judicial exception. The claims of the present application do not include additional elements that are sufficient to amount to significantly more than the judicial exception (NO).
In the case of claim 1, taken individually or as a whole, the additional elements of claim 1 do not provide an inventive concept. As discussed above under step 2A (prong 2) with respect to the integration of the abstract idea into a practical application, the additional elements used to perform the claimed functions amount to no more than a general link to a technological environment.
Even considered as an ordered combination (as a whole), the additional elements do not add anything significantly more than when considered individually.
Claim 12 is a computer program product reciting similar functions as claim 1. Examiner notes that claim 12 recites the additional elements of a computer program product, a non-transitory computer readable storage medium having instructions encoded thereon, a processor, the order classification model being trained, a computer system, the churn classification model being trained, trained and retrained by, a training dataset, score training examples, each score training example, a score training example, a loss function, backpropagation, a client device associated with the selected picker, a new training example, and training or retraining, however, claim 12 does not qualify as eligible subject matter for similar reasons as claim 1 indicated above.
Claim 20 is a computing system reciting similar functions as claim 1. Examiner notes that claim 20 recites the additional elements of the order classification model being trained, a computing system, one or more processors, a non-transitory computer readable storage medium having instructions encoded thereon, the churn classification model being trained, trained and retrained by, a training dataset, score training examples, each score training example, a score training example, a loss function, backpropagation, a client device associated with the selected picker, a new training example, and training or retraining, however, claim 20 does not qualify as eligible subject matter for similar reasons as claim 1 indicated above.
Even considered as an ordered combination (as a whole), the additional elements do not add anything significantly more than when considered individually.
Therefore, claims 12 and 20 do not provide an inventive concept and do not qualify as eligible subject matter.
Dependent claims 2-11 and 13-19, when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. § 101 because they do not add “significantly more” to the abstract idea. More specifically, dependent claims 2-11 and 13-19 further fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas in that they recite commercial interactions. Dependent claims 2-4, 10, 13, 15, and 19 do not recite any farther additional elements, and as such are not indicative of integration into a practical application for at least similar reasons discussed above. Dependent claims 5-9, 11, 14, and 16-18 recite the additional elements of the computer system, the churn classification model being trained, a training dataset, training examples, each training example, a loss function, the training example, backpropagation, a customer client device, the non-transitory computer readable storage medium, further has instructions encoded thereon, and the processor, but similar to the analysis under prong two of Step 2A these additional elements are used as a tool to perform the abstract idea. As such, under prong two of Step 2A, claims 2-11 and 13-19 are not indicative of integration into a practical application for at least similar reasons as discussed above. Thus, dependent claims 2-11 and 13-19 are “directed to” an abstract idea. Next, under Step 2B, similar to the analysis of claims 1, 12, and 20, dependent claims 2-11 and 13-19 when analyzed individually and as an ordered combination, merely further define the commonplace business method (i.e. identifying churn of a customer, identifying an event attributed to the churn, determining a picker to fulfill the subsequent order from the customer) being applied on a general-purpose computer and, therefore, do not amount to significantly more than the abstract idea itself. Accordingly, the Examiner concludes that there are no meaningful limitations in the claims that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself. The analysis above applies to all statutory categories of invention.
Subject Matter Allowable the Prior Art
In the present application, claims 1-20 would be allowable if rewritten or amended to overcome the rejections under 35 USC § 101 set forth in this Office action. The following is the Examiner's statement of reasons of allowance:
Regarding 35 U.S.C. §103, upon review of the evidence at hand, it is hereby concluded that the totality of the evidence, alone or in combination, neither anticipates, reasonably teaches, nor renders obvious the below noted features of the applicant’s invention. Claims 1-20 are allowable over the prior art as follows:
Claims 1-20 are allowable over 35 U.S.C. §103 as follows:
Claims 1-20 are allowable for the reasons detailed in the “Allowable Subject Matter” section of the Non-Final Office Action dated 08/26/2025.
The most relevant prior art made of record includes Paran et al. (US 11,775,865 B1), Ranjan et al. (US 2023/0214869 A1), and Agarwal et al. (US 2025/0124402 A1).
The most relevant NPL is:
Cited NPL reference U (cited 08/21/2025, 12/09/2025, 04/01/2026, and 09/04/2026 on PTO-892) teaches determining underlying factors for customer dropout, but does not teach or suggest the recited limitations.
Response to Arguments
Rejections under 35 U.S.C. §101
Applicant argues that Claim 1 has been amended to recite that orders classified as sporadic are eliminated from the set of orders used to identify churn of the customer and are excluded from the score training dataset used to train and retrain the picker scoring model. To further address this rejection, Applicant submits the Declaration of Brent Scheibelhut under 37 C.F.R. § 1.132 ("Declaration"), filed concurrently herewith. The Declaration establishes that the claimed use of a trained order classification model as a label-integrity gate is a technical solution to a recognized technical problem in the training of machine-learning models by backpropagation. As explained in the Declaration, ensuring the integrity of the labels in the training data used to fit a machine-learning model through backpropagation was a longstanding and well-recognized problem in the field of computer-implemented machine-learning systems well before the priority date of the application. Declaration at paragraph 5. The quality of the labels in the training data directly determines the loss signal computed during backpropagation and thus the parameters that the model learns at convergence. Id In systems that infer a customer behavioral state such as churn from records of prior interactions, labels are commonly derived from the elapsed time between successive customer actions, so that a long interval between orders is treated as a label indicating that churn occurred. Declaration at paragraph 6. However, certain orders are placed only in connection with atypical conditions of the customer, such as orders for holidays, birthdays, or anniversaries, and the long inter-order interval that follows such an order is expected and does not reflect a decrease in customer engagement. Id If such orders are not separated from the data used to identify churn and from the training dataset, the result is mislabeled training examples that introduce noise into the loss signal computed during backpropagation, causing the model to fit the spurious labels and degrading the predictive accuracy of the model's learned parameters. Declaration at paragraph 7 (Remarks, pages 15-16).
Examiner respectfully disagrees. The Declaration has been fully considered, however, even weighing all the evidence of record, the claims are ineligible. Merely removing data outliers to improve quality of data is not a problem inherent to the technological field of machine learning, rather it is a data analysis problem (i.e. not a technical problem). For instance, page 3 of Appendix B classifies “Data Cleansing,” which consists of excluding data to manage noisy labels, as “non-deep learning approach.” Additionally, MPEP 2106.05(a) makes it clear that “the judicial exception alone cannot provide the improvement.” Accordingly, providing more accurate data labels cannot provide the improvement to backpropagation. Thus, the claims are not integrated into a practical application.
Applicant further argues that the Declaration further explains how the claimed invention provides a particular solution to this problem. Specifically, the claimed invention interposes a trained order classification model as a label-integrity gate upstream of both the data used to identify customer churn and the score training dataset used to train and retrain the picker scoring model. Declaration at paragraph 8. Orders classified as sporadic are eliminated from the set of orders used to identify churn and are excluded from the score training dataset, which prevents mislabeled training examples arising from outlier sporadic orders from entering the backpropagation update loop of the picker scoring model. Declaration at paragraph 13. As a consequence, the loss signal computed during backpropagation more faithfully reflects the relationship between the inputs to the picker scoring model and the labels, the learned parameters are not biased by labels derived from sporadic orders, and the trained picker scoring model produces more accurate picker scores when it is subsequently applied to select a picker to fulfill a subsequent order from the customer. Id The Specification supports the same technical improvement. The Specification discloses that the online concierge system applies a trained order classification model to characteristic of the customer and attributes of an order to determine whether the order is sporadic, and that, in response to determining an order is sporadic, the system does not identify churn based on the interval following that order. Specification at, paragraphs 92-95. This confirms that the disclosed mechanism separates sporadic orders from the data on which churn is identified and the models are trained (Remarks, pages 16-17).
Examiner respectfully disagrees. The Declaration has been fully considered, however, even weighing all the evidence of record, the claims are ineligible. Again, merely removing data outliers to improve quality of data is not a problem inherent to the technological field of machine learning, rather it is a data analysis problem (i.e. not a technical problem). Page 3 of Appendix B classifies “Data Cleansing,” which consists of excluding data to manage noisy labels, as “non-deep learning approach.” MPEP 2106.05(a) further makes it clear that “the judicial exception alone cannot provide the improvement.” Accordingly, providing more accurate data labels cannot provide the improvement to backpropagation. Additionally, Applicant’s specification makes no mention of improving backpropagation through reduction of noisy labels, rather, Applicant’s specification merely describes classifying data such that outliers are removed (i.e. improving data rather than machine learning technology) and utilizing generic backpropagation methods. Thus, the claims are not integrated into a practical application.
Applicant further argues that, like the machine learning training method held eligible in Desjardins, the claimed invention improves how the machine-learning model itself operates. Specifically, the claimed invention interposes a trained order classification model as a label-integrity gate that filters mislabeled training examples out of the backpropagation update loop of the picker scoring model, thereby improving the predictive accuracy of the parameters the picker scoring model learns at convergence. The Specification and the Declaration together provide sufficient detail for a person of ordinary skill to recognize the claimed invention as a technical improvement. The Declaration explains that a person of ordinary skill in the art of machine-learning systems would recognize the interposition of a trained order classification model as a label-integrity gate as improving the predictive accuracy of the parameters that a downstream model learns at convergence, because it filters mislabeled training examples out of the backpropagation update loop. Declaration at , paragraphs 7-8, 13-14. The claims directly reflect the disclosed improvement. The limitations recite the specific mechanism the Declaration identifies as the technical improvement. A trained order classification model classifies orders placed in connection with an atypical condition of the customer as sporadic, those orders are eliminated from the set of orders used to identify churn and are excluded from the score training dataset, and the picker scoring model is then trained and retrained on the filtered dataset by scoring its output against the labels using a loss function and updating its parameters by backpropagation. The improvement is therefore reflected in the claim itself and is not confined to the Specification (Remarks, pages 17-19).
Examiner respectfully disagrees. The Declaration has been fully considered, however, even weighing all the evidence of record, the claims are ineligible. In Ex Parte Desjardins the claims were not found eligible because train the machine learning model in such a way that it “allows the model to preserve performance on earlier tasks even as it learns new ones, directly addressing the technical problem of 'catastrophic forgetting' in continual learning systems" (see Ex Parte Desjardins). The machine learning itself was improved. Unlike Ex Parte Desjardins, the recited claims merely filter data outliers to improve quality of data, this is not a technical solution. This is further supported by page 3 of Appendix B of the Declaration. Appendix B classifies “Data Cleansing,” which consists of excluding data to manage noisy labels, as “non-deep learning approach.” Additionally, Applicant’s specification makes no mention of improving backpropagation through reduction of noisy labels, rather, Applicant’s specification merely describes classifying data such that outliers are removed (i.e. improving data rather than machine learning technology) and utilizing generic backpropagation methods. Thus, the claims are not integrated into a practical application.
Applicant further argues that the Declaration further attests that the conventional approach to training a model that predicts customer-engagement outcomes from historical interaction data is to assemble training examples directly from the historical interaction records without first interposing a separately trained classifier as a label-integrity gate, and that the use of a trained order classification model in the manner recited in the claims is not well-understood, routine, or conventional in the field of computer-implemented machine-learning systems used to model recurring consumer transactions. Declaration at paragraph 13. This factual attestation is unrebutted, and under Berkheimer v. HP Inc., 881 F.3d 1360 (Fed. Cir. 2018), it establishes that the claimed combination is not a recitation of well-understood, routine, or conventional activity. For these reasons, the claims reflect the disclosed improvement in the technology of computer-implemented machine-learning systems and integrate any alleged abstract idea into a practical application. Accordingly, the claims recite patentable subject matter under Step 2A, Prong Two. (Remarks, page 19).
Examiner respectfully disagrees. The Declaration has been fully considered, however, even weighing all the evidence of record, the claims are ineligible. Page 3 of Appendix B included in the Declaration refers to “Data Cleansing” (i.e. excluding data to manage noisy labels) as a conventional technique that has been utilized for decades in the field of machine learning. The Declaration indicates that removing data outliers is, in fact, well-understood, routine, and conventional in the field of machine learning. Accordingly, the claims do not amount to significantly more than the abstract idea and are ineligible.
Applicant further argues that Claims 12 and 20 recite similar subject matter and are thus also patent eligible. The dependent claims are patent eligible by virtue of their dependency. In view of the above, this rejection should be withdrawn (Remarks, pages 19-20).
Examiner respectfully disagrees. As detailed in response to the arguments above, claim 1 is ineligible. Accordingly, independent claims 12 and 20, as well as, the dependent claims are ineligible.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
-Neumann et al. (US 12,073,637 B1) teaches removing training data classified as ‘bad.’
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/ARIELLE E WEINER/ Primary Examiner, Art Unit 3689
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